stevefoy
Work topics: Vision and Deep Learning. Researcher engineer in robots/vehicles/drones with big interest in Blender for design and simulation
Galway, Ireland
Pinned Repositories
3d-camera
Amazing-Semantic-Segmentation
Amazing Semantic Segmentation on Tensorflow && Keras (include FCN, UNet, SegNet, PSPNet, PAN, RefineNet, DeepLabV3, DeepLabV3+, DenseASPP, BiSegNet)
AMS_AS5048B
Arduino Lib for AMS AS5048B I2C - 14-bit magnetic rotary position sensor
Arduino-X9C
Arduino library for Intersil X9C series of digital potentiometers
ardupilot
ArduPlane, ArduCopter, ArduRover source
Mask_RCNN
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
NvidiaAIDenoiser
A simple implementation of Nvidia's AI denoiser
stevefoy's Repositories
stevefoy/caffe-segnet
Implementation of SegNet: A Deep Convolutional Encoder-Decoder Architecture for Semantic Pixel-Wise Labelling
stevefoy/eclipse-solarized
Solarized color theme for Eclipse.
stevefoy/ethzasl_ptam
Modified version of Parallel Tracking and Mapping (PTAM)
stevefoy/fast
FAST corner detector by Edward Rosten
stevefoy/glfw-cxx
glfw-cxx is a C++, Object Oriented wrapper for GLFW3.
stevefoy/LifeCLEF
Task Overview Following the success of the four previous plant identification tasks (ImageCLEF 2011-13 ; LifeCLEF 2014), we are glad to organize this year a new challenge dedicated to botanical data. The task will be focused on tree, herbs and ferns species identification based on different types of images. Its main novelties compared to the last years will by : -"use of external resources" : it will be possible to use more external online resources (but strictly forbidden to used data from Tela Botanica website), as training data to enrich the provided one, - "species number" : the number of species (about 1 000 species), which is an important step towards covering the entire flora of a given region. Multi-image query The motivation of the task is to fit better with a real scenario where one user tries to identify a plant by observing its different organs, such as it has been demonstrated in [MAED2012]. Indeed, botanists usually observe simultaneously several organs like the leaves and the fruits or the flowers in order to disambiguate species which could be confused if only one organ were observed. Moreover, if only one organ is observed, such as the bark of a deciduous plant during winter where nothing else is observable, then the observation of this organ with several photos related to different point of views could be more informative than only one point of view. Thus, contrary to the 3 first years, the species identification task won't be image-centered but OBSERVATION-centered. The aim of the task is be to produce a list of relevant species for each observation of a plant of the test dataset, i.e. one or a set of several pictures related to a same event: one same person photographing several detailed views on various organs the same day with the same device with the same lightening conditions observing one same plant.
stevefoy/neural2d
Neural net optimized for 2D image data
stevefoy/omniview
Small viewer for videos taken with an omnidirectional camera. Uses OCamCalib calibration files and OpenCV for undistorting the videos and provide a virtual camera view.
stevefoy/py_img_seg_eval
Evaluation metrics for image segmentation inspired by paper Fully Convolutional Networks for Semantic Segmentation
stevefoy/vorso_lab